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In this paper we design a system that adopts a novel approach for emotional classification from human dialogue based on text and speech context. Our main objective is to boost the accuracy of speech emotional classification by accounting for the features extracted from the spoken text. The proposed system concatenates text and speech features and feeds them as one input to the classifier. The work...
WLAN Device-free Passive (DfP) localization is an emerging technology that uses the widely deployed WiFi networks for detecting and localizing human presence within indoor environments. This paper presents an accurate and low-overhead technique for detecting human presence based on non-parametric statistical anomaly detection. This technique constructs profiles capturing the signal strength characteristics...
Performance of the speaker verification systems is typically measured based on their binary decision accuracy. However, in speaker verification applications where close to %100 accuracy is required, such as the systems that are used in the call centers of finance companies, it is not possible to rely on the binary decisions of the existing verification systems. Still, in such cases, multi-class verification...
Spatio-temporal interest points (STIPs) have recently become a mainstream technique for encoding human action video sequences. These local features overcome the complex issues of background subtraction and human tracking, and encode relevant motion information for recognition. Nonetheless, STIPs may result not only from actions themselves but from other factors such as person identity. This paper...
This study is a part of an ongoing project which aims to assist in teaching Sign Language (SL) to hearing-impaired children by means of non-verbal communication and imitation-based interaction games between a humanoid robot and a child. In this paper, the problem is geared towards a robot learning to imitate basic upper torso gestures (SL signs) using different machine learning techniques. RGBD sensor...
An extensive amount of research is being undertaken to gracefully solve the Human action recognition problem. To this end, in this paper, we introduce the application of self- similarity surfaces for human action recognition. These surfaces were introduced by Shechtman & Irani (CVPR'07) in the context of matching similarities between images or videos. These surfaces are obtained by matching a...
In this paper, we present a new way of generating behavioral (not biometric) fingerprints from the cellphone usage data. In particular, we explore if the generated behavioral fingerprints are memorable enough to be remembered by end users. We built a system, called HuMan, that generates fingerprints from cellphone data. To test HuMan, we conducted an extensive user study that involved collecting about...
Emotions are an important part of human communication and are expressed both verbally and non-verbally. Common nonverbal vocalizations such as laughter, cries and sighs carry important emotional content in conversations. Sighs often are associated with negative emotion. In this work, we show that emotional sighs exist along both ends of the valence axis (positive-emotion vs. negative-emotion sighs)...
In this work, we consider a classification problem of 14 physical activities using a body sensor network (BSN) consisting of 14 tri-axial accelerometers. We use a tree-based classifier, and develop a feature selection algorithm based on mutual information to find the relevant features at every internal node of the tree. We evaluate our algorithm on 31 features per accelerometer (total of 434), and...
The use of eye-tracking in energy-constrained applications requires systems capable of tracking human eyes without consuming large amount of power. In this paper we present a low-power implementation of real-time non-intrusive eye-tracking by single camera. Unlike existing hardware designs, our eye-tracker does not restrict the user nor requires computationally expensive tools for maintaining high...
We introduce a novel metric for speech recognition success in voice search tasks, designed to reflect the impact of speech recognition errors on user's overall experience with the system. The computation of the metric is seeded using intuitive labels from human subjects and subsequently automated by replacing human annotations with a machine learning algorithm. The results show that search-based recognition...
Computer lip-reading is one of the great signal processing challenges. Not only is the signal noisy, it is variable. However it is almost unknown to compare the performance with human lip-readers. Partly this is because of the paucity of human lip-readers and partly because most automatic systems only handle data that are trivial and therefore not representative of human speech. Here we generate a...
Biometrics systems based on ear are still in need of more investigation to make it robust and accurate. The most critical step in ear recognition is segmentation as all subsequent steps will depend on the accuracy of segmentation. In this paper, a robust ear segmentation method is suggested. The proposed method consists of a sequence of steps. First, a Biased Normalized Cuts method is applied to initiate...
Future interfaces that intend to support a Commander's decision-making process must be able to recommend prioritized actions such that the decision maker can dynamically focus attention in response to changing environments with uncertain information. A technique using the Choquet integral will be investigated that will monitor the environment by sampling current utility values of criteria and aggregate...
This paper proposes a soft voting based bag-of-features (BoF) model considering relative distance of the feature vectors to the nearest-neighbor codeword. Whereas state-of-the-art kernel distance based soft voting methods require brute force parameter optimization, which is time consuming, the proposed method does not require any optimization. The proposed algorithm was applied to human attribute...
In this work, we develop a computer vision based fall prevention system for hospital ward application. To prevent potential falls, once the event of patient get up from the bed is automatically detected, nursing staffs are alarmed immediately for assistance. For the detection task, we use a RGBD sensor (Microsoft Kinect). The geometric prior knowledge is exploited by identifying a set of task-specific...
This paper shows that pattern classification based on machine learning is a powerful tool for analyzing human brain activity data obtained by magnetoencephalography (MEG). In our previous work, a weighting method using multiple kernel learning was proposed, but this method had a high computational cost. In this paper, we propose a novel and fast weighting method using an AdaBoost algorithm to find...
An affective classification technology plays a key role in the affective human and computer interaction. This paper presents an affective classification method based on the Bayes classifier and the supervisory learning. We newly define a weighted-log-posterior function for the Bayes classifier, instead of the posterior function or the likelihood function that is used in the ordinary Bayes classifier...
We study the problem of human activity recognition from RGB-D sensors when the skeletons are not available. The skeleton tracking in Kinect SDK works well when the human subject is facing the camera and there are no occlusions. In surveillance or senior home monitoring scenarios, the camera is usually mounted higher than human subjects and there may be occlusions. Consequently, the skeleton tracking...
Pedestrian detection is an important task in many applications such as intelligent transportation systems, image retrieval, surveillance systems, automated personal assistance, etc. This paper proposes a set of modified Haar-like features that have parallelogram shapes. Using the proposed feature descriptors to develop a rapid detection system for pedestrian detection based on decision tree structure...
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